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The value of RDA outputs for a research institute specialized in - - PowerPoint PPT Presentation

Aug 20, 2023 229 likes •485 views

The value of RDA outputs for a research institute specialized in Agri-food sciences Odile Hologne, Head of the department of scientific information @Holo_08 In collaboration with : Imma Subirats (FAO), Sophie Aubin, Esther Dzal, Michael Chelle

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

The value of RDA outputs for a research institute specialized in Agri-food sciences

Odile Hologne, Head of the department of scientific information @Holo_08 In collaboration with : Imma Subirats (FAO), Sophie Aubin, Esther Dzalé, Michael Chelle (INRA)

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Challenges & missions of INRA

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INRA strategic framework (oct 2016)

Document

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Agenda  Data challenge in agri-food sciences  RDA outputs

 what we do  what we use

 Conclusion

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Data challenge in agri- food sciences

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Data in Agri-food sciences ?

Different families :

  • Omics
  • Observation
  • Social sciences, cohort
  • From genome to ecosystem
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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Data challenges in agri-food sciences

 Massive data production in labs (sensors, robots, models ….) but also in farms and by the citizens (of huge interest for science)  Big data : more variety than volume  Data silos, poorly documented, not easy to find, nor to access (same for semantic resources)  Different level of maturity of the practices about data (management, sharing, analysis)  Not only about data : code, workflow …  Disruption in the knowledge ecosystem (see next slide)

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Aubin S, RDA Agrisemantics Working Group and RDA Rice Data Interoperability Working Group. Semantics – The way to reconcile points of view and data [version 1; not peer reviewed]. F1000Research 2017, 6:1871 (poster) (doi: 10.7490/f1000research.1114998.1)

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Blind men exploring an elephant (Hokusai, 1818)

  • Big data technology

(store, query, compute)

  • FAIR data
  • Semantic level
  • Complex system
  • Simulation
  • Deep learning
  • Interoperability

(data / model)

  • Sharing
  • Collaborative

Digital

=> e-infrastructure

Agri-food science: The need to combine, integrate data!

Source : Michael Chelle, Inra

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

How to build a federative and distributed e- infrastructure to support agri-food sciences ?  Inra internal challenges:

 evolution of our digital infrastructure to provide « big data » and « FAIR data » services

  • New services
  • New skills

 Use external energies :

 Scientific communities, (i.e : plant sciences)  Funders : H2020 : EOSC projects, Belmont forum  Initiatives : GO FAIR, RDA

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What we do … What we use …

The role of RDA

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Objectives:

  • To promote good practices in the research domain:

data sharing policies, data management plan, data interoperability

  • To provide a platform for networking and cross-

fertilization of research ideas in data management and interoperability

  • To solicit and promote interactions and projects

among the major international institutions and groups worldwide which work

  • n

agricultural research and innovation

  • To achieve data interoperability

4 Working groups : Wheat Data Interoperability, Rice Data Interoperability, AgriSemantics, On-Farm Data Sharing, Capacity Development for Agricultural Research Data

Created in 2013, IGAD gather more than 200 people Co-chairs : Patricia Bertin (Embrapa, Brésil),

Imma Subirats (FAO-ONU, int.) web : https://bit.ly/2SW4TL6

A workshop before each RDA plenary Published

  • n

https://f1000research.com/gateways/g

  • dan/okad?selectedDomain=slides

Interest group “Agricultural Data” (IGAD)

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Co-chairs: Esther Dzalé (INRA, France), Richard Allan Fulss (CIMMYT, Mexico), Rosemary Shrestha (CIMMYT, Mexico)

  • Promote compliance of the data format with

available software applications (preferably

  • pen).
  • Facilitate recombination between sets of data

from different sources.

  • Promote common standards for metadata and

vocabularies to facilitate the interpretation and linking of data across disciplines.

  • Encourage mappings when unusual or project-

specific formats

  • r

vocabularies are unavoidable.

Improve wheat data interoperabilty

created in 2014, WDI is today a maintenance Group → recommandations continually updated

Wheat Data Interoperability WG

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Guidelines : web portal

http://wheatis.org/DataStandards.php

Ontologies collection in Agroportal

http://wheat.agroportal.lirmm.fr/ontologies

★ Recommended formats ★ Best practices to describe data ★ Best practices for data sharing ★ Tools

A prototype on AgroLD

http://www.agrold.org

★ Catalog of relevent ontologies

and vocabularies for wheat.

★ Give access to ontologies and

vocabularies via REST API and SPARQL endpoint.

★ Tools to search, align, …

An article

https://doi.org/10.12688/f1000research.12234.2

Wheat Data Interoperability WG Outputs

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

The Agrisemantics WG

  • Jan. 2017 - Mar. 2019, ~100 members

Objective of the group : Envision the seamless use and creation of semantic resources supporting agricultural and food data findability and interoperability

 A report on Semantics Landscape for Agricultural Data (applications, research trends, resources, toolkits) http://bit.ly/AgSemLandscape  A set of 20 use cases and a list of community requirements (access, reusability, tools and services for creation and management, use in applications, standards and best practices) http://bit.ly/AgSemReqUC  A document on Recommendations to facilitate the uptake of semantics for agricultural data (version submitted to the RDA TAB for endorsement) http://bit.ly/AgSemRecom

https://www.rd-alliance.org/groups/agrisemantics-wg.html

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What we use : some examples

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.017

The FAIRsharing Registry and Recommendations: Interlinking Standards, Databases and Data Policies

Odile Hologne, Sophie Aubin / JNSO/RDA France - Retours d’expérience

05 / 12 / 2018

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Horizon 2020 research and innovation programme - grant agreement No 730988

RDA 11th, Berlin, IGAD pre-meeting, 03/20/18

How to analyze a technical ecosystem ?

https://www.rd-alliance.org/sites/default/files/recommendation-jan-2017-v8.pdf

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New Library services inspired by 23 things …

 Information, training  Services for FAIR data : DMP, DOI,

  • ntologies

 Data portal based on dataverse http://datapartage.inra.fr http://data.inra.fr

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The whole story

https://www.rd-alliance.org/rda-adoption-story-inra-france

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Conclusion

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politics Funding

xxx €

EOSC calls

contribute use innovate Implement Influence

technics

0 €

RDA and the others …

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Conclusion  RDA is a win-win environment, at different scales :

 INRA perspective :

  • Institution : Useful results supporting our e-infra strategy,

specially in wheat research

  • Support team (library) : Increased the legitimity to work with

researchers

  • Individuals : results recognition

 RDA perspective :

  • Success stories easy to explain : feed 9 billion people …
  • An active community interacting with others – but also a

challenge to keep these interactions

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  • O. Hologne/ e-IRG Workshop – May 2019 – CERN, Geneva

Merci pour votre attention…

Odile.hologne@inra.fr

@Holo_08